Oil field operators dedicate significant resources to develop tools that help improve the overall production of oil and gas wells. Among such tools are computer-based models used to simulate the behavior of the fluids within a reservoir (e.g., water, oil and natural gas). These models enable operators to predict future production of the field as fluids are extracted and the field is depleted. To help ensure the accuracy of such predictions, the wells are periodically logged using production logging tools to update and maintain a historical database of relevant metrics for the wells within a field. Simulation model results may then be regularly correlated against the updated historical data, with modeling parameters being adjusted as needed to reduce the error between simulated and actual values.
Accurately matching a simulation to historical data, however, can be a challenging task given the number of modeling parameters, the complexity of their interactions, the uncertainty in the values of the parameters, and the non-uniqueness of model realizations that may match a given set of historical data. The process of history matching a simulation model may involve the execution of thousands of simulations, which can be computationally demanding for large fields with a large number of wells. While a number of stochastic techniques such as Bayesian sampling and Monte Carlo methods may be used to reduce the computational burden incurred to achieve a given level of accuracy, the continually increasing amount of data being sampled in the field and stored on historical databases, as well as the increasing complexity of the simulations themselves, continue to fuel the demand for systems and methods that decrease the number of simulation realizations needed to achieve a meaningful history match of a simulation model.
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